SoroushVahidi/scidocs huggingface dataset:
On error management: lessons from aviation.
(2000)

Copies of the full protocol and details of training programmes are available from Association of Litigation and Risk Management (ALARM), Royal Society of Medicine, 1 Wimpole Street, London W1. Contributors: CV and ST-A carried out the research on which the original protocol was based. All authors participated equally in the development of the protocol, in which successive versions were tested in clinical practice and refined in the light of experience. The writing of the original protocol and present paper was primarily carried out by CV, ST-A, EJC, and DH, but all authors contributed to the final version. CV and DH are the guarantors. Competing interests: CV received funding from Healthcare Risk Resources International to support the work of ST-A during the development of the protocol.
Self-assembly of neural networks viewed as swarm intelligence
(2009)

While self-assembly is a fairly active area of research in swarm intelligence, relatively little attention has been paid to the issues surrounding the construction of network structures. In this paper we extend methods developed previously for controlling collective movements of agent teams to serve as the basis for self-assembly or “growth” of networks, using neural networks as a concrete application to evaluate our approach. Our central innovation is having network connections arise as persistent “trails” left behind moving agents, trails that are reminiscent of pheromone deposits made by agents in ant colony optimization models. The resulting network connections are thus essentially a record of agent movements. We demonstrate our model’s effectiveness by using it to produce two large networks that support subsequent learning of topographic and feature maps. Improvements produced by the incorporation of collective movements are also examined through computational experiments. These results indicate that methods for directing collective movements can be adopted to facilitate network self-assembly.
Client Profiling for an Anti-Money Laundering System
(2015)

We present a data mining approach for profiling bank clients in order to support the process of detection of antimoney laundering operations. We first present the overall system architecture, and then focus on the relevant component for this paper. We detail the experiments performed on real world data from a financial institution, which allowed us to group clients in clusters and then generate a set of classification rules. We discuss the relevance of the founded client profiles and of the generated classification rules. According to the defined overall agent-based architecture, these rules will be incorporated in the knowledge base of the intelligent agents responsible for the signaling of suspicious transactions.
Strength Training for Endurance Athletes: Theory to Practice
(2015)

THE PURPOSE OF THIS REVIEW IS TWOFOLD: TO ELUCIDATE THE UTILITY OF RESISTANCE TRAINING FOR ENDURANCE ATHLETES, AND PROVIDE THE PRACTITIONER WITH EVIDENCED-BASED PERIODIZATION STRATEGIES FOR CONCURRENT STRENGTH AND ENDURANCE TRAINING IN ATHLETIC POPULATIONS. BOTH LOW-INTENSITY EXERCISE ENDURANCE (LIEE) AND HIGH-INTENSITY EXERCISE ENDURANCE (HIEE) HAVE BEEN SHOWN TO IMPROVE AS A RESULT OF MAXIMAL, HIGH FORCE, LOW VELOCITY (HFLV) AND EXPLOSIVE, LOW-FORCE, HIGH-VELOCITY STRENGTH TRAINING. HFLV STRENGTH TRAINING IS RECOMMENDED INITIALLY TO DEVELOP A NEUROMUSCULAR BASE FOR ENDURANCE ATHLETES WITH LIMITED STRENGTH TRAINING EXPERIENCE. A SEQUENCED APPROACH TOSTRENGTHTRAINING INVOLVING PHASES OF STRENGTHENDURANCE, BASIC STRENGTH, STRENGTH, AND POWER WILL PROVIDE FURTHER ENHANCEMENTS IN LIEE AND HIEE FOR HIGHLEVEL ENDURANCE ATHLETES.
Dual Switches DC/DC Converter With Three-Winding-Coupled Inductor and Charge Pump
(2016)

In order to obtain a high step-up voltage gain, high-efficiency converter, this paper proposed a dual switches dc/dc converter with three-winding-coupled inductor and charge pump. The proposed converter composed of dual switches structure, three-winding-coupled inductor, and charge pump. This combination facilitates realization of high step-up voltage gain with a low voltage/current stress on the power switches. Meanwhile, the voltage across the diodes is low and the diode reverse-recovery problem is alleviated by the leakage inductance of the three-winding-coupled inductor. Taking all these into consideration, the efficiency can be high. This paper illustrated the operation principle of the proposed converter; discussed the effect of leakage inductance on voltage gain; the conditions of zero current shutting off of the diodes are illustrated; the voltage and current stress of the power devices are shown; a comparison between the performance of the proposed converter and previous high step-up converters was conducted. Finally, a prototype rated at 500 W has been established, and the experimental results verify the correctness of the analysis.
Learning from positive and unlabeled examples
(2000)

In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant. Also, for some binary classification problems, positive examples which are elements of the target concept are available. Can these additional data be used to improve accuracy of supervised learning algorithms? We investigate in this paper the design of learning algorithms from positive and unlabeled data only. Many machine learning and data mining algorithms, such as decision tree induction algorithms and naive Bayes algorithms, use examples only to evaluate statistical queries (SQ-like algorithms). Kearns designed the statistical query learning model in order to describe these algorithms. Here, we design an algorithm scheme which transforms any SQ-like algorithm into an algorithm based on positive statistical queries (estimate for probabilities over the set of positive instances) and instance statistical queries (estimate for probabilities over the instance space). We prove that any class learnable in the statistical query learning model is learnable from positive statistical queries and instance statistical queries only if a lower bound on the weight of any target concept f can be estimated in polynomial time. Then, we design a decision tree induction algorithm POSC4.5, based on C4.5, that uses only positive and unlabeled examples and we give experimental results for this algorithm. In the case of imbalanced classes in the sense that one of the two classes (say the positive class) is heavily underrepresented compared to the other class, the learning problem remains open. This problem is challenging because it is encountered in many real-world applications. © 2005 Elsevier B.V. All rights reserved.
Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins.
(2003)

OBJECTIVE
Data on use and misuse of six classes of illicit substances by male twin pairs were used to examine whether genetic and shared environmental risk factors for substance use disorders are substance-specific or -nonspecific in their effect.


METHOD
Lifetime history of use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates was assessed at personal interview in both members of 1,196 male-male twin pairs ascertained by the Virginia Twin Registry. Multivariate twin modeling of substance-nonspecific (common) and substance-specific genetic, shared environmental, and unique environmental risk factors was performed by using the program Mx.


RESULTS
High levels of comorbidity involving the different substance categories were observed for both use and abuse/dependence. One common genetic factor was found to have a strong influence on risk for illicit use and abuse/dependence for all six substance classes. A modest influence of substance-specific genetic factors was seen for use but not for abuse/dependence. Shared environmental factors were more important for use than for abuse/dependence and were mediated entirely through a single common factor.


CONCLUSIONS
In an adult population-based sample of male twins, both the genetic and the shared environmental effects on risk for the use and misuse of six classes of illicit substances were largely or entirely nonspecific in their effect. Environmental experiences unique to the person largely determine whether predisposed individuals will use or misuse one class of psychoactive substances rather than another.
Question Answering on SQuAD
(2017)

In this project, we exploit several deep learning architectures in Question Answering field, based on the newly released Stanford Question Answering dataset (SQuAD)[7]. We introduce a multi-stage process that encodes context paragraphs at different levels of granularity, uses co-attention mechanism to fuse representations of questions and context paragraphs, and finally decodes the co-attention vectors to get the answers. Our best model gets 62.23% F1 score and 48.72% EM score on the test set.
Genetic Evidence That Carbohydrate-Stimulated Insulin Secretion Leads to Obesity.
(2018)

BACKGROUND
A fundamental precept of the carbohydrate-insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight.


METHODS
Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI.


RESULTS
Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10-21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30.


CONCLUSIONS
Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate-insulin model of obesity.
Slip-aware Model Predictive optimal control for Path following
(2016)

Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skid-steered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.
Datum: Managing Data Purchasing and Data Placement in a Geo-Distributed Data Market
(2018)

This paper studies two design tasks faced by a geo-distributed cloud data market: which data to purchase data purchasing and where to place/replicate the data for delivery data placement. We show that the joint problem of data purchasing and data placement within a cloud data market can be viewed as a facility location problem and is thus NP-hard. However, we give a provably optimal algorithm for the case of a data market made up of a single data center and then generalize the structure from the single data center setting in order to develop a near-optimal, polynomial-time algorithm for a geo-distributed data market. The resulting design, $\mathsf {Datum}$ , decomposes the joint purchasing and placement problem into two subproblems, one for data purchasing and one for data placement, using a transformation of the underlying bandwidth costs. We show, via a case study, that $\mathsf {Datum}$ is near optimal within 1.6% in practical settings.
Reducing Length of Stay Using a Robotic-assisted Approach for Retromuscular Ventral Hernia Repair: A Comparative Analysis From the Americas Hernia Society Quality Collaborative.
(2018)

OBJECTIVE
The aim of this study was to compare length of stay (LOS) after robotic-assisted and open retromuscular ventral hernia repair (RVHR).


BACKGROUND
RVHR has traditionally been performed by open techniques. Robotic-assisted surgery enables surgeons to perform minimally invasive RVHR, but with unknown benefit. Using real-world evidence, this study compared LOS after open (o-RVHR) and robotic-assisted (r-RVHR) approach.


METHODS
Multi-institutional data from patients undergoing elective RVHR in the Americas Hernia Society Quality Collaborative between 2013 and 2016 were analyzed. Propensity score matching was used to compare median LOS between o-RVHR and r-RVHR groups. This work was supported by an unrestricted grant from Intuitive Surgical, and all clinical authors have declared direct or indirect relationships with Intuitive Surgical.


RESULTS
In all, 333 patients met inclusion criteria for a 2:1 match performed on 111 r-RVHR patients using propensity scores, with 222 o-RVHR patients having similar characteristics as the robotic-assisted group. Median LOS [interquartile range (IQR)] was significantly decreased for r-RVHR patients [2 days (IQR 2)] compared with o-RVHR patients [3 days (IQR 3), P < 0.001]. No differences in 30-day readmissions or surgical site infections were observed. Higher surgical site occurrences were noted with r-RVHR, consisting mostly of seromas not requiring intervention.


CONCLUSIONS
Using real-world evidence, a robotic-assisted approach to RVHR offers the clinical benefit of reduced postoperative LOS. Ongoing monitoring of this technique should be employed through continuous quality improvement to determine the long-term effect on hernia recurrence, complications, patient satisfaction, and overall cost.
Possibilistic interest discovery from uncertain information in social networks
(2017)

User generated content on the microblogging social network Twitter continues to grow with significant amount of information. The semantic analysis offers the opportunity to discover and model latent interests’ in the users’ publications. This article focuses on the problem of uncertainty in the users’ publications that has not been previously treated. It proposes a new approach for users’ interest discovery from uncertain information that augments traditional methods using possibilistic logic. The possibility theory provides a solid theoretical base for the treatment of incomplete and imprecise information and inferring the reliable expressions from a knowledge base. More precisely, this approach used the product-based possibilistic network to model knowledge base and discovering possibilistic interests. DBpedia ontology is integrated into the interests’ discovery process for selecting the significant topics. The empirical analysis and the comparison with the most known methods proves the significance of this approach.
Mechanisms of emotional arousal and lasting declarative memory
(1998)

Neuroscience is witnessing growing interest in understanding brain mechanisms of memory formation for emotionally arousing events, a development closely related to renewed interest in the concept of memory consolidation. Extensive research in animals implicates stress hormones and the amygdaloid complex as key, interacting modulators of memory consolidation for emotional events. Considerable evidence suggests that the amygdala is not a site of long-term explicit or declarative memory storage, but serves to influence memory-storage processes in other brain regions, such as the hippocampus, striatum and neocortex. Human-subject studies confirm the prediction of animal work that the amygdala is involved with the formation of enhanced declarative memory for emotionally arousing events.
Human Activity Recognition Using Recurrent Neural Networks
(2017)

Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.
An Introductory Study on Time Series Modeling and Forecasting
(2013)

ACKNOWLEDGEMENT The timely and successful completion of the book could hardly be possible without the helps and supports from a lot of individuals. I will take this opportunity to thank all of them who helped me either directly or indirectly during this important work. First of all I wish to express my sincere gratitude and due respect to my supervisor Dr. R.K. Agrawal, Associate Professor SC & SS, JNU. I am immensely grateful to him for his valuable guidance, continuous encouragements and positive supports which helped me a lot during the period of my work. I would like to appreciate him for always showing keen interest in my queries and providing important suggestions. I also express whole hearted thanks to my friends and classmates for their care and moral supports. The moments, I enjoyed with them during my M.Tech course will always remain as a happy memory throughout my life. I owe a lot to my mother for her constant love and support. She always encouraged me to have positive and independent thinking, which really matter in my life. I would like to thank her very much and share this moment of happiness with her. Last but not the least I am also thankful to entire faculty and staff of SC & SS for their unselfish help, I got whenever needed during the course of my work.ABSTRACT Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effeciency of time series modeling and forecasting. The aim of this book is to present a concise description of some popular time series forecasting models used in practice, with their salient features. In this book, we have described three important classes of time series models, viz. the stochastic, neural networks and SVM based models, together with their inherent forecasting strengths and weaknesses. We have also discussed about the basic issues related to time series modeling, such as stationarity, parsimony, overfitting, etc. Our discussion about different time series models is supported by giving the experimental forecast results, performed on six real time series datasets. While fitting a model to a dataset, special care is taken to select the most parsimonious one. To evaluate forecast accuracy as well as to compare among different models …
On the development of name search techniques for Arabic
(2006)

The need for effective identity matching systems has led to extensive research in the area of name search. For the most part, such work has been limited to English and other Latin-based languages. Consequently, algorithms such as Soundex and n-gram matching are of limited utility for languages such as Arabic, which has a vastly different morphology that relies heavily on phonetic information. The dearth of work in this field is partly due to the lack of standardized test data. Consequently, we built a collection of 7,939 Arabic names, along with 50 training queries and 111 test queries. We use this collection to evaluate a variety of algorithms, including a derivative of Soundex tailored to Arabic (ASOUNDEX), measuring effectiveness using standard information retrieval measures. Our results show an improvement of 70% over existing approaches. Introduction Identity matching systems frequently employ name search algorithms to effectively locate relevant information about a given person. Such systems are used for applications as diverse as tax fraud detection and immigration control. Using names to retrieve information makes such systems susceptible to problems arising from typographical errors. That is, exact match search approaches will not find instances of misspelled names or those names that have more than one accepted spelling. An example of the severity of the problem is noted in an NCR (1998) report that estimates that the state of Texas saved $43 million over 18 months in the field of tax compliance using an improved name search approach. Thus, the importance of such name-based search applications has resulted in improved name matching algorithms for English that make use of phonetic information, but these language-dependent techniques have not been extended to Arabic.
Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach
(2018)

Tax fraud is the intentional act of lying on a tax return form with intent to lower one's tax liability. Under-reporting is one of the most common types of tax fraud, it consists in filling a tax return form with a lesser tax base. As a result of this act, fiscal revenues are reduced, undermining public investment.
 Detecting tax fraud is one of the main priorities of local tax authorities which are required to develop cost-efficient strategies to tackle this problem. Most of the recent works in tax fraud detection are based on supervised machine learning techniques that make use of labeled or audit-assisted data. Regrettably, auditing tax declarations is a slow and costly process, therefore access to labeled historical information is extremely limited. For this reason, the applicability of supervised machine learning techniques for tax fraud detection is severely hindered.
 Such limitations motivate the contribution of this work. We present a novel approach for the detection of potential fraudulent tax payers using only unsupervised learning techniques and allowing the future use of supervised learning techniques. We demonstrate the ability of our model to identify under-reporting taxpayers on real tax payment declarations, reducing the number of potential fraudulent tax payers to audit. The obtained results demonstrate that our model doesn't miss on marking declarations as suspicious and labels previously undetected tax declarations as suspicious, increasing the operational efficiency in the tax supervision process without needing historic labeled data.